Global Dated Landslide Data Base during Sentinel-2 satellite data availability
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This Global Dated Landslide Database (GDLDB) is part of the project WeMonitor (Weakly Supervised Deep Learning Models for Detecting and Monitoring Spatio-Temporal Anomalies in Optical and Radar Satellite Time Series), funded by the Helmholtz Imaging Platform. The aim is to develop a deep learning model that uses satellite image time series from Sentinel1/2 to automatically monitor changes caused, for example, by landslides, deforestation, large fires, dam failures, or the emergence of waste dumps. To train such a model, a reference dataset is required that shows the area and date of the changes as precise as possible. To allow for a generic and transferable model, the reference data also needs to cover the diversity of the process to be detected. Thus, the aim of the GDLDB is to comprise landslides of different sizes, shapes, and types, occurring at different seasons and in different regions with varying natural conditions and different triggering mechanisms such as rainfall and earthquake-induced landslides. To build the GDLDB, available local and regional landslide inventories from around the world are combined into one coherent database by verifying their location and date of occurrence with high-resolution remote sensing data. The selection criteria for the source inventories are the definition of the landslide location as polygons, at least a rough indication of the landslide origin date, and that the landslides occurred during the Sentinel-2 data availability from 2016 onwards. A total of 16 individual inventories are included (Table 1), one each from the USA, Dominica, Italy, Zimbabwe, southern India, Nepal, China, Papua New Guinea, and New Zealand, and two each from Kyrgyzstan, Japan, and the Philippines. In addition, a global inventory was added, including a small number of landslides from the USA, Peru, Chile, Europe, Pakistan, Nepal, India, and Taiwan, and a larger number of landslides from Indonesia. From each inventory, approximately 100 landslides were randomly selected to ensure an unbiased selection of landslides in terms of shape, size, and location. The original source inventories are produced using a variety of methods, including manual mapping in airborne data with ground verification and automatic identification in satellite remote sensing data. As a result, the mapping quality of the inventories varies greatly. In cases where landslides could not be verified by us using available optical remote sensing data (e.g. Sentinel-2, Planet Scope, and data available in Google Earth) new polygons are selected until the number of approximately 100 landslides is reached. In some inventories, the number of 100 landslides could not be guaranteed, due to a lack of suitable landslides (e.g., small size, incorrect classification) or the total number of landslides in the selected inventory was less than 100. For inventories with a lot of small landslides, that were difficult or impossible to observe, a size threshold of 1000m2 was introduced.
全球标注发生时间滑坡数据库（Global Dated Landslide Database，GDLDB）是亥姆霍兹成像平台（Helmholtz Imaging Platform）资助的WeMonitor项目（弱监督深度学习模型，用于检测与监测光学与雷达卫星时序数据中的时空异常）的组成部分。该项目旨在开发一款深度学习模型，利用哨兵1号/2号（Sentinel-1/2）卫星影像时序数据，自动监测滑坡、毁林、大规模火灾、溃坝或废弃物堆放场新增等引发的地表变化。为训练该模型，需构建一套参考数据集，尽可能精准地标注变化发生的区域与时间。为实现模型的通用性与可迁移性，参考数据集需覆盖待检测过程的各类变体。因此，GDLDB的构建目标是纳入不同尺寸、形态与类型的滑坡，涵盖不同季节、不同自然条件区域以及由降雨、地震等不同触发机制引发的滑坡。 为构建GDLDB，研究人员将全球范围内已有的本地与区域滑坡编目数据整合为统一数据库，通过高分辨率遥感数据验证各滑坡的发生位置与时间。源编目的遴选标准包括：滑坡位置以多边形标注、至少可大致确定滑坡发生日期，且滑坡发生时间处于2016年起哨兵2号卫星数据可获取的时段内。本次共纳入16套独立编目（见表1）：美国、多米尼克、意大利、津巴布韦、印度南部、尼泊尔、中国、巴布亚新几内亚、新西兰各1套，吉尔吉斯斯坦、日本、菲律宾各2套。此外，还补充了一套全球滑坡编目，其中包含少量来自美国、秘鲁、智利、欧洲、巴基斯坦、尼泊尔、印度及中国台湾地区的滑坡，以及大量来自印度尼西亚的滑坡。 从每套编目中随机选取约100处滑坡，以确保在滑坡形态、尺寸与分布上的遴选无偏性。原始源编目采用多种方法生成，包括结合地面验证的航空数据人工测绘，以及卫星遥感数据自动识别。因此，各编目的测绘质量差异显著。若部分滑坡无法通过现有光学遥感数据（如哨兵2号、Planet Scope及谷歌地球（Google Earth）中的公开数据）完成验证，则重新选取滑坡多边形，直至集齐约100处符合要求的滑坡。部分编目无法确保凑齐100处滑坡，原因包括缺乏符合要求的滑坡（如尺寸过小、分类错误），或所选编目内的滑坡总数不足100处。针对大量难以或无法观测的小型滑坡编目，研究人员设定了1000平方米的尺寸阈值。
